In spite of the best strategies for collecting data from sampled units, nonresponse nearly always occurs in population surveys, including those of lowincome families. A ''nonrespondent" is any sampled unit that is eligible for the study but for which data are not obtained for any reason. Failure to match the sample cases with the administrative files used to gather outcome data, refusal to participate in the survey, or situations such as ''notathome after multiple calls," ''language problems," and ''knowledgeable person not available" are some of the reasons why an eligible sampled unit may not participate in a survey. On the other hand, sampled units that are ineligible for the survey are not considered nonrespondents, even though they do not provide survey data. As discussed later in this section, nonrespondents and ineligibles are treated differently in the nonresponse adjustment process.
When nonresponse is present, a weight adjustment can partially compensate for the loss of data. This weight adjustment increases the weights of the sampled cases for which data were collected. The first step in adjusting for nonresponse is the construction of weighting classes. As discussed in the following text, within each weighting class, the base weights are inflated by the inverse of the response rate so that the sum of the adjusted base weights for respondents is equal to the sum of the base weights for the total eligible sample selected in the weighting class. Returning to the FIS example, assume that 160 families were selected (with equal probability) within a weighting class and that 77 families responded to the survey. Because the weight for each family is equal to 50 (as shown earlier), the nonresponseadjusted weight is about 104 (i.e., 50 multiplied by 160/77). Thus, after nonresponse adjustment each responding family in the sample represents about 104 families in the population within the weighting class.
The effectiveness of nonresponse adjustment procedures in reducing nonresponse bias is directly related to the ability to construct appropriate nonresponse adjustment classes. The following subsection provides a brief summary of two procedures commonly used to construct adjustment classes. The next subsection discusses samplebased adjustment procedures that are commonly used to compensate for nonresponse. Then we describe populationbased adjustment procedures (poststratification and raking) that are widely used for noncoverage adjustment, or sometimes used to correct simultaneously for both nonresponse and noncoverage. Additional benefits of populationbased adjustments include reduction in the sampling errors of the sample estimates as well as achieving consistency with the known population counts. In poststratification and raking, respondents are categorized according to one or more variables (e.g., age, gender, race, or income level) at a recent point in time, and the survey estimates are benchmarked to the known population totals. For a general review of weighting for nonresponse, refer to Elliot (1991). Finally, we provide a discussion of the importance of balancing bias and variance when adjusting survey data.

Construction of Nonresponse Adjustment Classes

Implementing nonresponse adjustment procedures requires the specification of appropriate weighting classes or cells. Survey responses generally are correlated with certain characteristics of the sample units, and it would be desirable to form classes based on these characteristics. Often, little is known about the nonrespondents. Relevant information about each sampled unit sometimes can be obtained through data retrieval efforts to collect limited data about the nonrespondents or by interviewer observation (if applicable). The availability of this information would enhance the effectiveness of the nonresponse adjustment.
Data used to form classes for nonresponse adjustments must be available for both respondents and nonrespondents. In state lowincome surveys, the administrative files used to select the sample are good sources of information for forming weighting classes. In a recent survey, we contacted a number of states to inquire about the availability and the quality of their administrative data, including the following variables:
 Demographic
 Age
 Gender
 Race/ethnicity
 Marital status
 Number of children
 Socioeconomic
 Education
 Employment
 Earned income
 Welfare income
 Housing subsidy
 Length of time on welfare
 Geographic
 Urban/rural
 Metropolitan/nonmetropolitan status
 County code
 Zip code
Thirteen states completed our questionnaire. All states reported having data on age, gender, race/ethnicity, number of children, and length of time on welfare. Most states also have data on earned income, welfare income, employment, county code, zip code, and marital status. About 50 to 60 percent of states reported having data on education, housing subsidies, metropolitan/nonmetropolitan status, and urbanicity. The 13 states that responded to the questionnaire on auxiliary data also indicated their assessments of the quality of the administrative data that their state maintains. We observed that the quality of data on demographic variables was quite high, with less than 1 percent missing values. For the socioeconomic variables, the only two variables with highquality data are ''welfare income" and ''length of time on welfare," where length of time on welfare is measured for the most recent episode. Data on employment and earned income, if applicable, were obtained by matching with quarterly wage records. The only geographic variables of high quality are county and zip codes. We encourage state welfare program administrators to look for other potential data sources that could be used as auxiliary variables for nonresponse and/or noncoverage adjustments, such as wages and employment data sources. The above variables are usually good candidates for use in nonresponse adjustment. However, missing data on items used for nonresponse adjustment can present problems for postsurvey adjustments. If a substantial amount of data are missing for an item on the sampling frame, this variable is probably not appropriate for the purpose of nonresponse adjustments.
The variables used to form weighting classes should be effective in distinguishing between subgroups with different response rates. They are most useful when survey responses are roughly similar for respondents and nonrespondents within a class. If this implicit assumption holds, the estimates are effectively unbiased. In establishing the nonresponse adjustment classes, the following should be kept in mind:
 The variables used in nonresponse adjustment should be available for both respondents and nonrespondents;
 Response rates should be different among the nonresponse adjustment classes;
 Survey responses are expected to be different among the classes; and
 The adjustment classes should respect a balance between bias and variance (refer to the section entitled ''Balancing Bias and Variance When Adjusting for Nonresponse" for a discussion of balancing bias and variance when creating adjusted sampling weights).
As mentioned earlier, knowledge of the likely behavior of persons in various demographic and socioeconomic classes can be used to construct weighting classes. A preliminary analysis of response rates in these classes can refine the classification further.
Returning to the FIS example provided earlier, assume that nonresponse evaluation research has identified the gender and race (white/nonwhite) of the head of family as the best predictors of nonresponse. Then, the sample is divided into four classes, as shown in Table 54. Note that mean income and the nonresponse rate are both quite variable across the four classes. This suggests that the adjustments have the potential to reduce the nonresponse bias.
TABLE 54
Nonresponse Adjustment Classes for the FIS ExampleAdjustment Class Head of Family's
Gender and RaceSample Size Respondent
Mean Income ($)Respondents Nonresponse
Rate (%)Male 1 White 160 1,712 77 52 2 Non White 51 1,509 35 31 Female 3 White 251 982 209 17 4 Non White 358 911 327 9 Total 820 1,061 648 21 NOTES: *Family Income Survey(FIS) More sophisticated methods also are available. We discuss two commonly used procedures (referred to as modeling response propensity) for defining weighting classes using data on auxiliary variables. The first method involves classification or segmentation based on a categorical search algorithm. The second method is based on logistic regression modeling. Software is available to perform the computations required for both procedures.
The first class of methods divides a population into two or more distinct groups based on categories of the ''best" predictor of a dependent variable. The dependent variable is a categorical variable with two categories: respondents and nonrespondents. The predictor variable with the highest significance level is used to split the sample into groups. It then splits each of these groups into smaller subgroups based on other predictor variables. This splitting process continues until no more statistically significant predictors can be found, or until some other stopping rule is met (e.g., there are too few observations for further splitting). The result is a treelike structure that suggests which predictor variable may be important.^{(5)} It is a highly efficient statistical technique for segregation, or tree growing, with many different versions currently available, as described in Breiman et al., (1993).
The second approach models the response status of the sampled units using predictor variables that are known for both respondents and nonrespondents from the sampling frame. Most commonly, the prediction approach is based on a logistic or probit regression model effectively using auxiliary variables, such as demographic, socioeconomic, and geographic variables, to predict the probability of response. For more information on logistic response propensity modeling, refer to Little and Rubin (1987), Brick and Kalton (1996), and Iannacchione et al. (1991).
 Demographic


SampleBased Adjustment Procedures

Samplebased nonresponse adjustments make use of information that is available for the sample, and thus do not require any external population counts. In effect, samplebased nonresponse adjustments distribute the base weights of the nonresponding units to the responding sampled units so that the sum of the adjusted weights over the responding units equals the sum of the base weights for the entire sample.
The basic form of the samplebased nonresponse adjustments is a ratio of sums of base weights where the sums extend over specified subsets of the sample defined by response status. The particular form of the adjustment depends on whether the eligibility status of the nonresponding units can be ascertained. First, we describe the nonresponse adjustment under the assumption that every sampled unit can be assigned to one of the following three response status groups:
Group 1: Respondents. This group consists of all eligible sample units that participated in the survey (i.e., provided usable survey data).
Group 2: Nonrespondents. This group consists of all eligible sample units that did not provide usable survey data.
Group 3: Ineligible or out of scope. This group consists of all sample units that were ineligible or out of scope for the survey.
In this particular case, it is assumed that all of the nonrespondents (Group 2) in the sample have been determined to be eligible for the survey and that all of those in Group 3 have been determined to be ineligible for the survey. If eligibility is unknown for some of the selected cases, the usual approach is to distribute proportionally the weights of those with unknown eligibility to those for which eligibility was determined. In the FIS example, let's assume that 850 families originally were selected from an administrative file. However, it was determined later that 30 families were ineligible because the administrative frame was outdated, for example. The total number of eligible families is 820, and 648 responded to the survey. In this case, Group 1 = 648, Group 2 = 172, and Group 3 = 30. The corresponding samplebased nonresponse adjustment factoris defined to be the ratio of sums:
where W_{i}_{}is the base weight for the sampled unit i, R represents survey respondents (Group 1), and N represents nonrespondents (Group 2). The adjustment factor is applied only to the base weights of the respondents (Group 1) in the sample; that is, the nonresponseadjusted weight is computed as
= 0, if unit i is nonrespondent (Group 2).
= W_{i}_{}, if unit i is out of scope (Group 3).
if unit i is an eligible respondent (Group 1).
In practice, the nonresponse adjustment,, is calculated within specified weighting or adjustment classes. The procedures for forming appropriate weighting classes for this purpose were discussed earlier.
Table 55 shows the nonresponse adjustment factors and adjusted weights for the FIS example. Because the base weights are equal to N/n (=50) for each sampled family (as shown in an earlier section on base weights), the nonresponse adjustment factors in column 4 are simply equal to the ratio of column 2 to column 3. The base weights would be adjusted by multiplying the base weights by the nonresponse adjustment factors i.e., column 1 multiplied by column 4. That is, the adjusted weight for each of the respondents in the four cells created by gender and race is equal to the weight given in column (5).
TABLE 55
Nonresponse Adjustment Factors and the Adjusted Weights for the FIS* ExampleHead of Family's
Gender and RaceBase Weight
(1)Sample Size
(2)Respondents
(3)Nonresponse Adjustment Factor
(4)Adjusted Weight**
(5)Male White 50 160 77 2.08 104 Non white 50 51 35 1.46 73 Female White 50 251 209 1.20 60 Non white 50 358 327 1.10 55 Total 820 648 NOTES:
* Family Income Survey
** For presentation purposes, we have rounded up the adjustment factors (to two decimals) and the adjusted weights (to whole numbers). The calculations, however, carry all the decimals.


PopulationBased Adjustments

In applications where external control counts are available for weighting, the usual practice is to first calculate samplebased nonresponseadjusted weights and then to further adjust these weights through populationbased adjustments. Populationbased adjustment tends to reduce the effects of noncoverage (e.g., incomplete frames) and improve the representation of the sample. Sometimes, it is convenient or necessary to bypass the intermediate step of calculating the samplebased nonresponseadjusted weights. In this case, the base weights would be ratio adjusted directly to known control totals in a single step. For example, if the classes used for nonresponse adjustment also are used for populationbased adjustments, the twostep procedure of first adjusting for nonresponse and then adjusting to known control totals is equivalent to the single populationbased adjustment procedure discussed in this section. Separate nonresponse adjustments are necessary when the nonresponse weighting classes are different from those planned for the populationbased adjustments. This is usually, although not always, the case because different sources of data are available for each adjustment. In the following sections, we briefly describe the two most commonly used populationbased adjustment procedures.
The term ''calibration" is used in the literature to cover a variety of techniques used in benchmarking the weights to known external totals. In this paper, we focus our attention on the two procedures most commonly used in general surveys: poststratification and raking.
Poststratification
Poststratification is a popular estimation procedure in which the weights of the respondents are adjusted further so that the sums of the adjusted weights are equal to known population totals for certain subgroups of the population. For example, take the case where the population totals of subgroups (referred to as poststrata) defined by age, gender, and race/ethnicity are known from the sampling frame (or other external sources), and they also can be estimated from the survey. Poststratification adjusts the survey weights so that the distribution by subgroups (when weighted by the poststratified weights) is the same as the population distribution from the survey frame or external sources.
Letdenote the population count in the poststratum denoted by g as obtained from the sampling frame or an external source, and let be the corresponding survey estimate obtained by using the nonresponseadjusted weights. Then the ratiois the poststratification adjustment factor for subgroup g .
The main advantage of poststratification is that the procedure reduces the bias from some types of noncoverage and nonresponse. An additional advantage of poststratification is the improvement in the reliability of the survey estimates for variables that are highly correlated with the variables used for poststratification. Generally, the poststratified weights are the final survey weights, and these would be used to tabulate the survey results. Occasionally, an additional weighting factor, called a ''trimming factor," is used to protect against extremely high variances. A brief description of trimming procedures used in practice is provided in a later section. If a trimming factor is calculated for a survey data file, it should be incorporated into the final weight as another multiplication factor.
Earlier, we illustrated the nonresponse adjustment procedure by assuming that the number of families in the population was 41,000 and that there was no noncoverage. We continue the FIS example, assuming that the number of families in the population was actually 46,000 and that the sampling frame contained only 41,000 families because information necessary for locating respondents was missing for 5,000 families. However, some limited demographic and other socioeconomic information was available in the data files for all 46,000 families. Suppose further that the noncoverage rate varies within the four cells defined by the crossclassification of employment status (employed/not employed) and education (high school diploma/no high school diploma) of the head of the family. Poststratification adjustment can be applied to reduce the bias arising from noncoverage.
The poststratification adjustment factor for a poststratification cell is the ratio of the known family count within the poststratification cell to the corresponding estimate of the family count from the survey. The estimate of the family count within a poststratification cell is obtained by summing the nonresponseadjusted weights of the families (as shown in Table 55) in the poststratification cell. Because the base weights were adjusted to account for the nonresponse (as given in Table 55), these adjusted weights would vary by poststratified adjustment classes. Therefore, Table 56 gives the count and the adjusted weight for the 16 cells defined by the crossclassification of nonresponse adjustment classes (4 classes) and poststrata (4 cells).
Column 2 is the nonresponse adjusted weight for each family in the gender/race/ employment/education class. The initial estimate of total number of families in each class (taking nonresponse into account) is the product of columns 1 and 2 and is given in column 3. The total of the nonresponseadjusted weights (column 3) can be used to estimate the number of families by poststrata defined by employment status and education of the head of the family. Table 57 provides the estimates of the family count and the corresponding known family count from external sources by poststrata. The table also gives the poststratification adjustment factors, defined as the ratio of the known family count and the survey estimate.
TABLE 56
Distribution of NonresponseAdjusted Weights by Gender, Race, Employment, and Education for the FIS* ExampleHead of family Respondent Count
(1)NonresponseAdjusted Weight**
(2)Initial Estimated No. of Families
(3)Gender and Race Employment Education Male White Employed HS *** 38 104 3,948 Non White Employed HS 15 73 1,093 White Employed No HS 11 104 1,143 Non White Employed No HS 6 73 437 White No HS diploma HS 12 104 1,247 Non White No HS diploma HS 5 73 364 White Unemployed No HS 16 104 1,662 Non White Unemployed No HS 9 73 656 Female White Employed HS 101 60 6,065 Non White Employed HS 158 55 8,649 White Employed No HS 30 60 1,801 Non White Employed No HS 47 55 2,573 White Unemployed HS 33 60 1,982 Non White Unemployed HS 51 55 2,792 White Unemployed No HS 45 60 2,702 Non White Unemployed No HS 71 55 3,887 Total 648 41,000 NOTES:
* Family Income Study
** For presentation purposes, adjusted weights are rounded to whole numbers. The calculations, however, carry all the decimals.
*** HS =High school diplomaTABLE 57
Poststratification Adjustment Factors for the FIS* ExamplePoststratum Initial Survey Estimate* Known Auxiliary Total Adjustment Factor** Employed HS *** 19,757 22,125 1.12 No HS 5,955 6,313 1.06 Unemployed HS 6,385 6,966 1.09 No HS 8,908 10,596 1.19 NOTES:
* Family Income Study
** For presentation purposes, we have rounded up the adjustment factors (to two decimals) and the adjusted weights (to whole numbers). The calculations, however, carry all the decimals.
*** HS =High school diplomaThe final survey weights are defined as the product of the base weight and the adjustment factors for nonresponse and poststratification. Table 58 includes the final weights for the FIS example. The final weight in column 5 is equal to the product of the base weight in column 1 and the nonresponse adjustment in column 3 and the poststratification factor in column 4.
It is not always possible to use poststratification because it requires data on the crossclassification of categorical variables that are used to define poststrata. Either the celllevel population counts may not be available or the sample sizes for some of the cells in the poststrata may not be adequate (for a discussion of adequate cell sample sizes, refer to the following section entitled ''Balancing Bias and Variance When Adjusting for Nonresponse"). In such situations, survey practitioners frequently use a more complex poststratification method, referred to as a raking procedure, which adjusts the survey estimates to the known marginal totals of several categorical variables.
Raking Procedure
This methodology is referred to as raking ratio estimation because an iterative procedure is used to produce adjustment factors that provide consistency with known marginal population totals. Typically, raking is used in situations where the interior cell counts of a crosstabulation are unknown or the sample sizes in some cells are too small for efficient estimation (refer to the following section for more information about sufficient cell sample size).
Raking ratio estimation is based on an iterative proportional fitting procedure developed by Deming and Stephan (1940). It involves simultaneous ratio adjustments of sample data to two or more marginal distributions of the population counts. With this approach, the weights are calculated such that the marginal distribution of the weighted totals conforms to the marginal distribution of the targeted population; some, or all, of the interior cells may differ.
The raking procedure is carried out in a sequence of adjustments. The base weights (or nonresponseadjusted weights) are first adjusted to produce one marginal distribution, the adjusted weights are used to produce a second marginal distribution, and so on, up to the number of raking dimensions. One sequence of adjustments to the marginal distributions is known as a cycle or iteration. The sequence of adjustments is repeated until convergence is achieved, meaning that the weights no longer change with each iteration. In practice, the raking procedure usually converges, but the number of iterations may be large when there are many marginal distributions involved in raking.
TABLE 58
Final Poststratified Weights for the FIS* ExamplePoststratum Base
Weight(1)Respondents
(2)NonResponse
Adjustment(3)**Post Strat.
Adjust Ment(4)**Final Weight
(5)**Final Estimate of
No. of Families (6)Head of Family's
Gender and RaceEmployment Education Male White Employed HS *** 50 38 2.08 1.12 116 4,422 Non white Employed HS 50 15 1.46 1.12 82 1,224 White Employed No HS 50 11 2.08 1.06 110 1,212 Nonwhite Employed No HS 50 6 1.46 1.06 77 463 White Unemployed HS 50 12 2.08 1.09 113 1,360 Non white Unemployed HS 50 5 1.46 1.09 80 397 White Unemployed No HS 50 16 2.08 1.19 124 1,978 Non white Unemployed No HS 50 9 1.46 1.19 87 780 Female White Employed HS 50 101 1.20 1.12 67 6,793 Non white Employed HS 50 158 1.10 1.12 62 9,687 White Employed No HS 50 30 1.20 1.06 64 1,910 Non white Employed No HS 50 47 1.10 1.06 58 2,728 White Unemployed HS 50 33 1.20 1.09 65 2,162 Non white Unemployed HS 50 51 1.10 1.09 60 3,046 White Unemployed No HS 50 45 1.20 1.19 71 3,215 Non white Unemployed No HS 50 71 1.10 1.19 65 4,624 Total 648 46,000 NOTES:
* Family Income Survey
** For presentation purposes, we have rounded up the adjustment factors (to two decimals) and the adjusted weights (to whole numbers). The calculations, however, carry all the decimals.
*** HS =High school diplomaThe final weights are produced automatically by the software that implements raking. The raking procedure only benchmarks the sample to known marginal distributions of the population; it should not be assumed that the resulting solution is ''closer to truth" at the crossclassification cell level as well. The final solution from a raking procedure may not reflect the correlation structure among different variables. For a more complete discussion of raking, refer to Kalton and Kasprzyk (1986).
As noted earlier, raking is one of a range of related methods known as calibration methods. One specific calibration method is GREG (Generalized REGression). GREG is not as commonly used as poststratification and raking because of its rather complex application and some of its limitations. Refer to Särndal et al. (1992) and Valliant et al. (2000) for a description of GREG.^{(6)} For information about calibration techniques, refer to Deville and Särndal (1992) and Theberge (2000).
The weighting system is implemented by assigning weights to each person (or family) in the sample, inserting the weight into the computer record for each person, and incorporating the weights in the estimation process using software created for survey data analysis.


Balancing Bias and Variance When Adjusting for Nonresponse

The fundamental objective of the design of any survey sample is to produce a survey data set, that, for a given cost of data collection, will produce statistics that are nearly unbiased and sufficiently precise to satisfy the goals of the expected analyses of the data. In general, the goal is to keep the mean square error (MSE) of the primary statistics of interest as low as possible. The MSE of a survey estimate is
MSE = Variance + (Bias)^{2} . [5]
The purpose of the weighting adjustments discussed in this paper is to reduce the bias associated with noncoverage and nonresponse in surveys. Thus, the application of weighting adjustments usually results in lower bias in the associated survey statistics, but at the same time adjustments may result in some increases in variances of the survey estimates.
The increases in variance result from the added variability in the sampling weights due to nonresponse and noncoverage adjustments. Thus, the analysts who create the weighting adjustment factors need to pay careful attention to the variability in the sampling weights caused by these adjustments. The variability in weights will reduce the precision of the estimates. Thus, a tradeoff should be made between variance and bias to keep the MSE as low as possible. However, there is no exact rule for this tradeoff because the amount of bias is unknown.
In general, weighting class adjustments frequently result in increases in the variance of survey estimates when (1) many weighting classes are created with a few respondents in each class, and (2) some weighting classes have very large adjustment factors (possibly due to much higher nonresponse or noncoverage rates in these classes). To avoid such situations, survey statisticians commonly limit the number of weighting classes created during the adjustment process. In general, although exact rules do not exist for minimum sample sizes or adjustment factors for adjustment cells, statisticians usually avoid cells with fewer than 20 or 30 sample cases or adjustment factors larger than 1.5 to 2. Refer to Kalton and Kasprzyk (1986) for more information on this topic.
Occasionally, the procedures used to create the weights may result in a few cases with extremely large weights. Extreme weights can seriously inflate the variance of survey estimates. ''Weight trimming" procedures are commonly used to reduce the impact of such large weights on the estimates produced from the sample.
Weight trimming refers to the process of adjusting a few extreme weights to reduce their impact on the weighted estimates (i.e., increase in the variances of the estimates). Trimming introduces a bias in the estimates; however, most statisticians believe that the resulting reduction in variance decreases the MSE. The inspection method, described in Potter (1988, 1990), is a common trimming method used in many surveys. This method involves the inspection of the distribution of weights in the sample. Based on this inspection, outlier weights are truncated at an acceptable level (the acceptable level is derived based on a tradeoff between bias and variance). The truncated weights then are redistributed so that the total weighted counts still match the weighted total before weight trimming.
Analysts should pay attention to the variability of the weights when working with survey data, even though all measures (such as limits on adjustment cell sizes, and weight trimming) may have been taken to keep the variability of weights in moderation. Analysts should keep in mind that large variable values in conjunction with large weights may result in extremely influential observations, that is, observations that dominate the analysis.

View full report
"01.pdf" (pdf, 472.92Kb)
Note: Documents in PDF format require the Adobe Acrobat Reader®. If you experience problems with PDF documents, please download the latest version of the Reader®
View full report
"02.pdf" (pdf, 395.41Kb)
Note: Documents in PDF format require the Adobe Acrobat Reader®. If you experience problems with PDF documents, please download the latest version of the Reader®
View full report
"03.pdf" (pdf, 379.04Kb)
Note: Documents in PDF format require the Adobe Acrobat Reader®. If you experience problems with PDF documents, please download the latest version of the Reader®
View full report
"04.pdf" (pdf, 381.73Kb)
Note: Documents in PDF format require the Adobe Acrobat Reader®. If you experience problems with PDF documents, please download the latest version of the Reader®
View full report
"05.pdf" (pdf, 393.7Kb)
Note: Documents in PDF format require the Adobe Acrobat Reader®. If you experience problems with PDF documents, please download the latest version of the Reader®
View full report
"06.pdf" (pdf, 415.3Kb)
Note: Documents in PDF format require the Adobe Acrobat Reader®. If you experience problems with PDF documents, please download the latest version of the Reader®
View full report
"07.pdf" (pdf, 375.49Kb)
Note: Documents in PDF format require the Adobe Acrobat Reader®. If you experience problems with PDF documents, please download the latest version of the Reader®
View full report
"08.pdf" (pdf, 475.21Kb)
Note: Documents in PDF format require the Adobe Acrobat Reader®. If you experience problems with PDF documents, please download the latest version of the Reader®
View full report
"09.pdf" (pdf, 425.17Kb)
Note: Documents in PDF format require the Adobe Acrobat Reader®. If you experience problems with PDF documents, please download the latest version of the Reader®
View full report
"10.pdf" (pdf, 424.33Kb)
Note: Documents in PDF format require the Adobe Acrobat Reader®. If you experience problems with PDF documents, please download the latest version of the Reader®
View full report
"11.pdf" (pdf, 392.39Kb)
Note: Documents in PDF format require the Adobe Acrobat Reader®. If you experience problems with PDF documents, please download the latest version of the Reader®
View full report
"12.pdf" (pdf, 386.39Kb)
Note: Documents in PDF format require the Adobe Acrobat Reader®. If you experience problems with PDF documents, please download the latest version of the Reader®
View full report
"13.pdf" (pdf, 449.86Kb)
Note: Documents in PDF format require the Adobe Acrobat Reader®. If you experience problems with PDF documents, please download the latest version of the Reader®
View full report
"14.pdf" (pdf, 396.87Kb)
Note: Documents in PDF format require the Adobe Acrobat Reader®. If you experience problems with PDF documents, please download the latest version of the Reader®